The Influence of Fold Structure on Heat Accumulation in Hot Dry Rocks

Hot dry rock resources as one of the most promising clean energy in the future, with large reserves, renewable and other advantages, since the 1970 s, many countries all over the world have explored and practiced a lot on the exploration and development of hot dry rock resources, however, few studied the heterogeneity of the rock and the underground geologic structures of hot dry rock resources influence domain enrichment regularity of heat transfer mechanism. Therefore, this article considered the thermal conductivity of rock anisotropy, and set up a horizontal stratum and a fold strata 2D geological model, through numerical simulation with the field rock samples indoor triaxial rock thermal conductivity test results, introducing the thermal conductivity of rock anisotropy index A = K vertical bedding/ K parallel bedding and analyze the underground geologic structures’ influence on heat transfer in the rock. The results show that the anisotropy of rock thermal conductivity has no influence on the heat transfer process in underground rock strata when the rock layer is horizontal, which can be regarded as one-dimensional multilayer wall heat transfer. Fold structure will influence the underground heat transfer direction, so it is not simply seen as a one-dimensional multilayer flat wall heat transfer process in numerical simulation. At the inclined interface of rock strata, "heat flow refraction" usually occurs, which further affects the direction of heat transfer. As a result, heat is concentrated in the syncline of the fold structure in the deep and anticline in the middle and deep layers, while the temperature distribution in the shallow layer is almost unaffected by the structure. The research results of this paper are of great significance to the delineation of the target area and the development and utilization of the hot dry rock resources.

Robust well-log based determination of rock thermal conductivity through machine learning

SUMMARY Rock thermal conductivity is an essential input parameter for enhanced oil recovery methods design and optimization and for basin and petroleum system modelling. Absence of any effective technique for direct in situ measurements of rock thermal conductivity makes the development of well-log based methods for rock thermal conductivity determination highly desirable. A major part of the existing problem solutions is regression model-based approaches. Literature review revealed that there are only several studies performed to assess the applicability of neural network-based algorithms to predict rock thermal conductivity from well-logging data. In this research, we aim to define the most effective machine-learning algorithms for well-log based determination of rock thermal conductivity. Well-logging data acquired at a heavy oil reservoir together with results of thermal logging on cores extracted from two wells were the basis for our research. Eight different regression models were developed and tested to predict vertical variations of rock conductivity from well-logging data. Additionally, rock thermal conductivity was determined based on Lichtenecker–Asaad model. Comparison study of regression-based and theoretical-based approaches was performed. Among considered machine learning techniques Random Forest algorithm was found to be the most accurate at well-log based determination of rock thermal conductivity. From a comparison of the thermal conductivity—depth profile predicted from well-logging data with the experimental data, and it can be concluded that thermal conductivity can be determined with a total relative error of 12.54 per cent. The obtained results prove that rock thermal conductivity can be inferred from well-logging data for wells that are drilled in a similar geological setting based on the Random Forest algorithm with an accuracy sufficient for industrial needs.